Introduction

In this assignment, I will be working with the gapminder dataset to (1) Reorder a factor, (2) Write some data to file and load it back into R, (3) Improve a figure using new knowledge, (4) Make a plotly visual and (5) Implement visualization design principles.

Let’s start by uploading all the neccessary packages.

suppressPackageStartupMessages(library(tidyverse))
## Warning: package 'tidyverse' was built under R version 3.5.1
## Warning: package 'ggplot2' was built under R version 3.5.1
## Warning: package 'tibble' was built under R version 3.5.1
## Warning: package 'tidyr' was built under R version 3.5.1
## Warning: package 'readr' was built under R version 3.5.1
## Warning: package 'purrr' was built under R version 3.5.1
## Warning: package 'dplyr' was built under R version 3.5.1
## Warning: package 'stringr' was built under R version 3.5.1
## Warning: package 'forcats' was built under R version 3.5.1
suppressPackageStartupMessages(library(gapminder))
## Warning: package 'gapminder' was built under R version 3.5.1
suppressPackageStartupMessages(library(RColorBrewer))
suppressPackageStartupMessages(library(scales))
## Warning: package 'scales' was built under R version 3.5.1
suppressPackageStartupMessages(library(plotly))
## Warning: package 'plotly' was built under R version 3.5.1

Part 1: Factor management

Drop Oceania

The goal of this assignment is the following: > Filter the Gapminder data to remove observations associated with the continent of Oceania. Additionally, remove unused factor levels. Provide concrete information on the data before and after removing these rows and Oceania; address the number of rows and the levels of the affected factors.

Summarizing data before changes

Let’s check the general structure of the gapminder dataset by usingstr function.

str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

We can notice that we have 6 variables and 1704 observations: country and continent are factor variables, year and pop are integers, and lifeExp and gdpPercap are numeric.

Let’s check which levels are present in the continent variable.

levels(gapminder$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

We can also measure the number of levels - 5 in total.

nlevels(gapminder$continent)
## [1] 5

Now, let’s count the number of observations by continent in the gapminder dataset.

ggplot(gapminder, aes(continent, fill = continent)) +
  geom_bar() +
  geom_text(stat = 'count', aes(label = ..count..),position = position_stack(vjust = 0.5)) +
  labs(title="Number of observations in gapminder by continent")

Removing Oceania observations

Knowing the main characteristics of the dataframe, we can filter the Gapminder data to remove observations associated with the continent of Oceania by using the filter function. We will then count how many observations in each continent category are left.

gapminder_no_oceania <- gapminder %>%
  filter(continent!="Oceania")
## Warning: package 'bindrcpp' was built under R version 3.5.1
ContinentTable <- table(gapminder_no_oceania$continent)
knitr::kable(ContinentTable, col.names = c("Continent", "Count"), format = "markdown")
Continent Count
Africa 624
Americas 300
Asia 396
Europe 360
Oceania 0

We can notice that all the observations in the Oceania category have been dropped, yet it is still unclear whether Oceania category is still an existing level. Let’s check how many levels we have in the continent variable.

levels(gapminder_no_oceania$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

Oceania is still part of the continent variable even though it has no observations. We will have to use the droplevels() to remove all the unused levels.

gapminder_continents <- gapminder_no_oceania %>% 
  droplevels()

Checking updated dataframe

Now, let’s check if Oceania is still present as a level.

levels(gapminder_continents$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"

Not anymore! Let’s see what the new barchart looks like.

ggplot(gapminder_continents, aes(continent, fill = continent)) +
  geom_bar() +
  geom_text(stat = 'count', aes(label = ..count..),position = position_stack(vjust = 0.5)) +
  labs(title="Number of observations in gapminder by continent")

We can now take a look at the characteristics of our new dataframe.

str(gapminder_continents)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1680 obs. of  6 variables:
##  $ country  : Factor w/ 140 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 4 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

We can notice that we still have 6 variables but only 1680 observations (a decrease of 24 from the initial 1704 observations): country has now 140 levels instead of 142 and continent has 4 levels instead of 5.

Order by Another Variable

The goal of this component is to: > Use the forcats package to change the order of the factor levels, based on a principled summary of one of the quantitative variables.

Here, I will reorder the levels of country variable by the max gdpPercap.

Here is a plot of gdpPercap of European countries. We can notice that countries are sorted alphabetically, making it not very useful to analyze various characteristics of this variable.

gapminder_Europe <- gapminder %>% 
  filter(continent == "Europe")

ggplot(gapminder_Europe, aes(gdpPercap, country)) +
  geom_point(aes(color=year)) +
  labs(title = "GDP per capita in European countries",
       x = "GDP per capita",
       y = "Country")

I’d like to sort these countries based on the max gdpPercap to produce an updated plot using the mutate and fct_reorder functions.

gapminder_Europe_sorted <- gapminder_Europe %>% 
  mutate(country = fct_reorder(country, gdpPercap, .fun = max))

ggplot(gapminder_Europe_sorted, aes(gdpPercap, country)) + 
  geom_point(aes(color=year)) +
  labs(title = "GDP per capita in European countries, sorted",
       x = "GDP per capita",
       y = "Country")

From here, we can notice that Norway had the highest maxgdpPercap and Albania had the lowerst maxgdpPercap among all European other countries.

Part 2: File I/O

In this assignment, I will do the following: > Experiment with one or more of write_csv()/read_csv() (and/or TSV friends), saveRDS()/readRDS(), dput()/dget() and explore whether this survives the round trip of writing to file then reading back in.

Here, we will try to expirent with downloading and uploading the dataframe we just created gapminder_Europe_sorted using three different file types.

CSV

Download the new data frame.

write_csv(gapminder_Europe_sorted, "gapminder_Europe_sorted.csv")

Upload the downloaded data frame.

read_csv("gapminder_Europe_sorted.csv") %>% 
ggplot(aes(gdpPercap, country)) + 
  geom_point(aes(color=year)) +
  labs(title = "GDP per capita in European countries, sorted",
       x = "GDP per capita",
       y = "Country")
## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )

The data is not arranged by max gdpPercap anymore, meaning that the file didn’t survive the transformation back and forth.

RDS

Let’s check if RDS functions preserve the sorted arrangement.

Download the new data frame.

saveRDS(gapminder_Europe_sorted, "gapminder_Europe_sorted.rds")

Upload the downloaded data frame.

readRDS("gapminder_Europe_sorted.rds") %>% 
ggplot(aes(gdpPercap, country)) + 
  geom_point(aes(color=year)) +
  labs(title = "GDP per capita in European countries, sorted",
       x = "GDP per capita",
       y = "Country")

The data is sorted as initially expected. RDS functions are able to preserve the sorted format of the data.

Dput/Dget

Finally, let’s check the third option using dput() and dget() functions.

Download the new data frame.

dput(gapminder_Europe_sorted, file="gapminder_Europe_sorted")

Upload the downloaded data frame.

dget("gapminder_Europe_sorted") %>% 
ggplot(aes(gdpPercap, country)) + 
  geom_point(aes(color=year)) +
  labs(title = "GDP per capita in European countries, sorted",
       x = "GDP per capita",
       y = "Country")

The arrangement of observations is also preserved.

Part 3: Visualization design

The goal of this assignment is to: > Remake at least one figure or create a new one, in light of something you learned in the recent class meetings about visualization design and color. Reflect on the differences. Then, make a new graph by converting this visual to a plotly graph.

Remake a figure

Here is a scatterplot of gdpPercap and lifeExp for European countries between 1970 and 1994 I completed in homework 2:

gapminder %>% 
  filter(continent == "Europe") %>% 
  filter(year>1970 & year<1994)  %>% 
  ggplot(aes(gdpPercap, lifeExp, color=year))+
  geom_point()+
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

I did the following to improve the scatterplot: 1. Created a new variable for the final plot.
2. Changed the selection parameters by removing the restrictions on years. 3. The size of points is now connected to the population size. 4. Made points somewhat transparent. 5. Added titles to the graph, renamed x-axis and y-axis. 6. Changed the colour of the smoothing funciton, made it transparent, changed the size of the line. 7. Added dollar sign format to x-axis values. 8. Used a new colour scheme for years: scale_colour_viridis_c. 9. Made sure the population size was reflected in a correct format on a side. 10. Added a new theme: theme_bw()

gapminder_Europe_gdpPercap <- gapminder_Europe %>% 
  ggplot(aes(gdpPercap, lifeExp, color=year, size=pop, alpha=0.2)) +
  geom_point() +
  labs(title = "Scatterplot of GDP per Capita and Life Expectancy (Europe)",        x = "GDP per Capita",
       y = "Average Life Expectancy") +
  geom_smooth(fill="purple", alpha=0.3, size=0.7) + 
  scale_x_log10(labels=dollar_format()) +
  scale_colour_viridis_c() +
  scale_size_continuous(
        labels = comma_format())+
  theme_bw()

gapminder_Europe_gdpPercap
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

We can observe a strong linear relationship between gdpPercap and lifeExp.

Next, I wanted do this plot for all other continents with countries whose gdpPercap is between $5,000 and $1,0000.

gapminder_gdpPercap <- gapminder %>% 
  filter(gdpPercap>5000 & gdpPercap <10000) %>% 
  ggplot(aes(gdpPercap, lifeExp, color=year, size=pop, alpha=0.2)) +
  geom_point() +
  facet_wrap(~continent) +
  geom_smooth(method='lm', size=0.5, alpha=0.2)+
  labs(title = "Scatterplot of GDP per Capita and Life Expectancy",  
       x = "GDP per Capita",
       y = "Average Life Expectancy") +
  scale_x_log10(labels=dollar_format()) +
  scale_colour_viridis_c() +
  scale_size_continuous(
        labels = comma_format())+
  theme_bw()+
  theme(strip.background = element_rect(fill = "yellow"),
        strip.text = element_text(color = "black"), 
        axis.text = element_text(size=9))

gapminder_gdpPercap

We can notice here one of the continents doesn’t have data that satisfies this requirement (Oceania). There is also no longer a linear relationship between gdpPercap and lifeExp - this pattern holds true for all four continents.

Plotly

Finally, let’s convert our two generated plots into the plotly format so we can interact with it.

Let’s make gapminder_Europe_gdpPercap interactive. A few added changes: 1. Year will appear as a moving format so color will be connected to the population size. 2. Country names will appear on each point.

  gapminder_Europe_plotly <- gapminder_Europe %>% 
  ggplot(aes(gdpPercap, lifeExp, color=pop, alpha=0.2, frame = year)) +
  geom_point(aes(size=pop, ids = country)) +
  labs(title = "Scatterplot of GDP per Capita and Life Expectancy (Europe)",        x = "GDP per Capita",
       y = "Average Life Expectancy") +
  geom_smooth(method="lm", fill="purple", alpha=0.15, size=0.7) + 
  scale_x_log10(labels=dollar_format()) +
  scale_colour_viridis_c(trans   = "log10",
        breaks  = 10^(1:10),
        labels  = comma_format()
    ) +
  scale_size_continuous(
        labels = comma_format())+
  theme_bw()
## Warning: Ignoring unknown aesthetics: ids
gapminder_Europe_plotly <- ggplotly(gapminder_Europe_plotly )
gapminder_Europe_plotly

For the gapminder_gdpPercap we no longer have to restrict it to a small number of countries as the year variable can appear as a frame. We will exclude Oceania as it only has 2 countries.

gapminder_gdpPercap_plotly <- gapminder %>% 
  filter(continent!="Oceania") %>% 
  ggplot(aes(gdpPercap, lifeExp, color=pop, frame=year, alpha=0.2)) +
  geom_point(aes(size=pop,ids=country)) +
  facet_wrap(~continent) +
  geom_smooth(method='lm', size=0.5, alpha=0.2)+
  labs(title = "Scatterplot of GDP per Capita and Life Expectancy",  
       x = "GDP per Capita",
       y = "Average Life Expectancy") +
  scale_x_log10(labels=dollar_format()) +
  scale_colour_viridis_c(trans   = "log10",
        breaks  = 10^(1:10),
        labels  = comma_format()
    ) +
  scale_size_continuous(
        labels = comma_format())+
  theme_bw()+
  theme(strip.background = element_rect(fill = "yellow"),
        strip.text = element_text(color = "black"), 
        axis.text = element_text(size=9))
## Warning: Ignoring unknown aesthetics: ids
gapminder_gdpPercap_plotly <- ggplotly(gapminder_gdpPercap_plotly)
gapminder_gdpPercap_plotly

Part 4: Writing figures to file

Use ggsave() to explicitly save a plot to file. Then use to load and embed it in your report. You can play around with various options, such as:

ggsave(“./gdpPercap_vs_lifeExp_Thai_Viet.png”, plot = plot4)

Arguments of ggsave(), such as width, height, resolution or text scaling. Various graphics devices, e.g. a vector vs. raster format. Explicit provision of the plot object p via ggsave(…, plot = p). Show a situation in which this actually matters.